Asynchronous Particle Swarm Optimization Research Articles

A parallel particle swarm optimization framework based on a fork-join thread pool using a work-stealing mechanism

Particle Swarm Optimization (PSO) is one of the most popular optimization algorithms that has been adopted in various fields, including design, scheduling, and biochemistry. However, the algorithm is time-consuming when facing high-dimensional or multi-objective optimizing problems. Parallel PSO is thus proposed to improve its computing efficiency, and many studies have been conducted. However, the low-level system design is seldom considered in parallel programming, which may have a nonnegligible impact on computing efficiency, creating a gap between low-level optimization and high-level algorithm design. Therefore, this paper proposes a Parallel Asynchronous PSO (PAPSO) framework based on thread pools utilizing multicore processors and adopts a cross-level approach to bridge the gap. A series of experiments are designed and conducted to examine how the aforementioned method can improve the parallel execution efficiency of PSO compared with the OpenMP framework and nonparallel PSO. Results indicate that PAPSO can significantly improve PSO computing efficiency by reducing the elapsed time, approaching approximately 20% optimization compared with OpenMP. Additionally, it achieves an approximately linear speedup of up to 4.5 threads. In addition, the particle communication experiment shows that the nonblocking communication protocol is effective for maintaining the computing elapsed time in the same level facing different neighborhood sizes. Finally, the work-stealing mechanism achieves an average of 16% improvement for general computing scenarios and maintain up to 16% improvement for imbalanced workload scenarios. Generally, the major contribution of this paper is that we innovate the thread pooling management concept with the fork-join model in parallelizing PSO to make sufficient use of multiple core CPUs for parallel computing through multithread programming.

Electromagnetic Imaging for Buried Conductors Using Deep Convolutional Neural Networks

In the past, many conventional algorithms, such as self-adaptive dynamic differential evolution and asynchronous particle swarm optimization, were used to reconstruct buried objects in the frequency domain; these were unfortunately time-consuming during the iterative, repeated computing process of the scattered field. Consequently, we propose an innovative deep convolutional neural network approach to solve the electromagnetic inverse scattering problem for buried conductors in this paper. Different shapes of conductors are buried in one half-space and the electromagnetic wave from the other half-space is incident. The shape of the conductor can be reconstructed promptly by inputting the received scattered fields measured from the upper half-space into the deep convolutional neural network module, which avoids the computational complexity of Green’s function for training. Numerical results show that the root mean square error for differently shaped—circular, elliptical, arrow, peanut, four-petal, and three-petal—reconstructed images are, respectively, 2.95%, 3.11%, 17.81%, 15.10%, 14.14%, and 15.24%. Briefly speaking, not only can circular and elliptical buried conductors be reconstructed; some irregular shapes can be reconstructed well. On the contrary, the reconstruction result by U-Net for buried objects is worse since it is not able to obtain a good preliminary image by processing only the upper scattered field—that is, rather than the full space. In other words, our proposed deep convolutional neural network can efficiently solve the electromagnetic inverse scattering problem of buried conductors and provide a novel method for the microwave imaging of the buried conductors. This is the first successful attempt at using deep convolutional neural networks for buried conductors in the frequency domain, which may be useful for practical applications in various fields such as the medical, military, or industrial fields, including magnetic resonance imaging, mine detection and clearance, non-destructive testing, gas or wire pipeline detection, etc.

A Hybrid Approach based on PSO and Boosting Technique for Data Modeling in Sensor Networks

An efficient data aggregation approach in wireless sensor networks (WSNs) is to abstract the network data into a model. In this regard, regression modeling has been addressed in many studies recently. If the limited characteristics of the sensor nodes are omitted from consideration, a common regression technique could be employed after transmitting all the network data from the sensor nodes to the fusion center. However, it is not practical nor efferent. To overcome this issue, several distributed methods have been proposed in WSNs where the regression problem has been formulated as an optimization based data modeling problem. Although they are more energy efficient than the centralized method, the latency and prediction accuracy needs to be improved even further. In this paper, a new approach is proposed based on the particle swarm optimization (PSO) algorithm. Assuming a clustered network, firstly, the PSO algorithm is employed asynchronously to learn the network model of each cluster. In this step, every cluster model is learnt based on the size and data pattern of the cluster. Afterwards, the boosting technique is applied to achieve a better accuracy. The experimental results show that the proposed asynchronous distributed PSO brings up to 48% reduction in energy consumption. Moreover, the boosted model improves the prediction accuracy about 9% on the average.

Asymmetric Bouc-Wen hysteresis modeling for MFC actuator via hybrid APSO-TRR identification algorithm

Macro fiber composite (MFC) is widely used in active vibration and deformation control. The intrinsic asymmetric hysteresis nonlinearity of MFC affects the control accuracy. In this paper, a modified Bouc-Wen (MBW) model based on the sigmoid function is proposed to describe the asymmetric hysteresis characteristics of MFC, and its parameters are identified by a hybrid algorithm composed of the trust-region reflection method and asynchronous particle swarm optimization. The accuracy of the proposed MBW model is verified though hysteresis tests of MFC under different drive frequencies. The results show that the MBW model can accurately model the asymmetrical hysteresis of the MFC actuator, the modeling error is reduced by 72% compared with the classic Bouc-Wen model. The proposed hybrid parameter identification method saves 95% of the time compared with the particle swarm optimization and asynchronous particle swarm optimization algorithms. • A modified Bouc-Wen (MBW) model is proposed to describe the asymmetric hysteresis characteristics of MFC actuators. • A hybrid algorithm (APSO - TRR) with global and local search ability is proposed for parameter identification. • Performance of proposed APSO - TRR is compared with classical PSO、APSO and TRR algorithms. • Accuracy of proposed asymmetric Bou–Wen model is compared with classical Bou–Wen model.

Research on identification of unbalance parameters of rotor with multi-plane using improved particle swarm optimization

Based on Bentley RK4 test rig, the method of nonlinear particle swarm optimization (PSO) is used to solve inverse problem instead of previous methods, which reduces identification complexity and avoids the ill posed problem caused by the singular equation in the process of solving. This paper realized model-based standard particle swarm optimization (SPSO) algorithm after modelling of the finite element model of rotor system and creating the optimization variables and individual fitness function, and then established asynchronous adaptive particle swarm optimization with optimizing speed weight and search scope. In order to further improve algorithm accuracy, chaos weighted particle swarm optimization and double chaos particle swarm optimization (DCPSO) are established. The simulation results of four methods show that DCPSO can realize accurate identification of unbalanced parameters with an average error of 2.86%. The effectiveness and rationality of this algorithm are verified from the speed of 240–3000 r/min. Particularly, at 2040 r/min, the percentage of amplitude decrease in two measuring points is 94.07% and 95.93%, which has achieved excellent vibration suppression effect.

Inverse scattering of a periodic inhomogeneous dielectric scatterer

ABSTRACT This paper is to reconstruct the periodic inhomogeneous dielectric distribution of the scatterer buried in the rough surface. In order to explore the dielectric coefficient distribution of the unknown dielectric object under the rough surface, we emit electromagnetic wave to the object and measure the scattered electromagnetic wave above the rough surface. Base on Green's identity and the induced current concepts, the nonlinear integral equation can be derived and solved by the method of moments. Next, the inverse scattering problem is converted into an optimization problem. We use Self-Adaptive Dynamic Differential Evolution (SADDE) and Asynchronous Particle Swarm Optimization (APSO) to find the extreme value of the problem. With the regularization technique, the reconstruction is good. When the noise is less than 1%, the dielectric constant can also be achieved successfully. Numerical results show that SADDE can reduce the error for the permittivities of the object better than the APSO.

Management of Multiple-Transmitter Multiple-Receiver Wireless Power Transfer Systems Using Improved Current Distribution Control Strategy

For high-power single-transmitter single-receiver wireless power transfer (STSRWPT) systems, the coils suffer from high voltage and current stresses. With increased power requirements, the coils become bottlenecks for power flow. To increase the power level, multiple-transmitter multiple-receiver wireless power transfer (MTMRWPT) systems with parallel circuits are developed that reduce the voltage and current stresses on the coils and improve power-handling capability. Firstly, an improved current distribution (ICD) control strategy is developed to simultaneously achieve high transfer efficiency, balanced current distribution and constant output voltage. Secondly, it is further shown that the ICD control strategy has the advantage that the currents at the transmitter coils are balanced and it reduces the control complexity simultaneously. Thirdly, an asynchronous particle swarm optimization (APSO) algorithm is applied to the ICD control strategy to verify the feasibility of the proposed control strategy. Lastly, a two-transmitter two-receiver wireless power transfer (WPT) system based on the ICD control strategy is proved to obtain an efficiency of more than 89.1% and provides the target output voltage 20 V with balanced current distribution.

Evolution algorithms applied for 3D beamforming UWB channels

In this paper, the main research object is evolution algorithm applied for an ultra-wideband three-dimensional (3D) circular antenna array and the significance of elevation angles and their influence on the system performance are highlighted. The 3D channel is calculated by means of 3D shooting and bouncing ray/image techniques which specify multipath elevation angles and azimuth for horizontal plane angles. The capacities of the two-dimensional (2D) and three-dimensional transmitting antenna arrays have been presented. Beam-synthesizing techniques are applied at the transmitter to focus the transmitter energy for the sake of mitigating the multi-path effect and improving channel capacity. A combination of asynchronous particle swarm optimization (APSO) and dynamic differential evolution (DDE) methods are introduced to make adjustment to the length of the feed line on each array element for the maximum capacity. In comparison with 2D azimuth-only antenna arrays, a higher directive gain can be achieved from the 3D antenna arrays results in high channel capacity. Besides, it is discovered that the capacity for the 3D array improves by as much as over 10% when compared to that for the 2D array. As revealed by numerical results, APSO is capable of obtaining better beam-forming pattern to improve the channel capacity than DDE by about 9%. Not only does our research provide qualitative outcomes, it also presents quantitative results.

Random Synchronous Asynchronous PSO – A Particle Swarm Optimization Algorithm with a New Iteration Strategy


 
 
 
 Particle swarm optimisation (PSO) is a population-based stochastic optimisation algorithm. Traditionally the particles update sequence for PSO can be categorized into two groups, synchronous (S-PSO) or asynchronous (A-PSO) update. In S-PSO, the particles’ performances are evaluated before their velocity and position are updated, while in A-PSO, each particle’s velocity and position is updated immediately after individual performance is evaluated. In another study, a random asynchronous PSO (RA-PSO) has been proposed. In RA-PSO, particles are randomly chosen to be updated asynchronously, the randomness improves swarm’s exploration. RA-PSO belongs to the asynchronous group. In this paper, a new category; hybrid update sequence is proposed. The new update sequence exploits the advantages of synchronous, asynchronous, and random update methods. The proposed sequence is termed as, random synchronous-asynchronous PSO (RSA-PSO). RSA-PSO divides the particles into groups. The groups are subjected to random asynchronous update, while the particles within a chosen group are updated synchronously. The performance of RSA-PSO is compared with the existing S-PSO, A-PSO, and RA-PSO using CEC2014’s benchmark functions. The results show that RSA-PSO is superior to both A-PSO and RA-PSO, and as good as S-PSO
 
 
 

Simultaneous Model Order and Parameter Estimation (SMOPE) based on Random Asynchronous Particle Swarm Optimization


 
 
 
 Simultaneous model order and parameter estimation (SMOPE) is a metaheuristic based system identification method. SMOPE was introduced using particle swarm optimization (PSO). There are several iteration strategies for PSO. The original work on SMOPE is based on synchronous PSO (S-PSO). However, in some works PSO using other iteration strategy is found to give better results. In this work, based on six system identification problems random asynchronous (RA-PSO) based SMOPE is found to have slight advantage over S-PSO.
 
 
 

Cooperative Asynchronous Parallel Particle Swarm Optimization for Large Dimensional Problems

Metaheuristics have been very successful to solve NP-hard optimization problems. However, some problems such as big optimization problems are too expensive to be solved using classical computing. Naturally, the increasing availability of high performance computing (HPC) is an appropriate alternative to solve such complex problems. In addition, the use of HPC can lead to more accurate metaheuristics if their internal mechanisms are enhanced. Particle swarm optimization (PSO) is one of the most know metaheuristics and yet does not have many parallel versions of PSO which take advantage of HPC via algorithmic modifications. Therefore, in this article, the authors propose a cooperative asynchronous parallel PSO algorithm (CAPPSO) with a new velocity calculation that utilizes a cooperative model of sub-swarms. The asynchronous communication among the sub-swarms makes CAPPSO faster than a parallel and more accurate than the master-slave PSO (MS-PSO) when the tested big problems.

A new asynchronous reinforcement learning algorithm based on improved parallel PSO

As an important machine learning method, reinforcement learning plays a more and more important role in practical application. In recent years, many scholars have studied parallel reinforcement learning algorithm, and achieved remarkable results in many applications. However, when using existing parallel reinforcement learning to solve problems, due to the limited search scope of agents, it often fails to reduce the running episodes of algorithms. At the same time, the traditional model-free reinforcement learning algorithm does not necessarily converge to the optimal solution, which may lead to some waste of resources in practical applications. In view of these problems, we apply Particle swarm optimization (PSO) algorithm to asynchronous reinforcement learning algorithm to search for the optimal solution. First, we propose a new asynchronous variant of PSO algorithm. Then we apply it into asynchronous reinforcement learning algorithm, and proposed a new asynchronous reinforcement learning algorithm named Sarsa algorithm based on backward Q-learning and asynchronous particle swarm optimization (APSO-BQSA). Finally, we verify the effectiveness of the asynchronous PSO and APSO-BQSA algorithm proposed in this paper through experiments.

Optimal Location of the Access Points for MIMO-UWB Systems

A multiple-input and multiple-output ultra-wideband (MIMO-UWB) system provides a higher data rate. However, the multipath effect of the intersymbol interference (ISI) increases the bit error rate (BER) and outage probability of the MIMO-UWB system. For this paper, the authors applied the real orthogonal design (ROD) to an MIMO-UWB system to improve the efficiency of that system. A ray-tracing technique and an inverse fast Fourier transform were used to get the impulse response of the indoor environment. In addition, a rake receiver was used to increase the strength of the received signal to minimize the multipath effect. For this paper, two cases of an indoor wireless MIMO-UWB system were studied: case (A) used different antenna arrays, whereas case (B) placed antenna arrays in different locations to find the best position of the transmitter. In case (A), three different shapes of antenna arrays, namely L-shape, circular-shape, and Y-shape, were used for the transmitter and receiver. The BER performance for these arrays in the UWB frequency of 3.1–10.6 GHz was examined. Numerical results showed that the outage probability of the circular array was better than that of the other two arrays. In case (B), the transmitter used was an array with two antenna elements. The optimal location for the transmitter was found by using both asynchronous particle swarm optimization (APSO) and self-adaptive dynamic differential evolution (SADDE). The numerical results indicated that the performance of APSO was better than that of SADDE.

Heterogeneous computing for epidemiological model fitting and simulation

BackgroundOver the last years, substantial effort has been put into enhancing our arsenal in fighting epidemics from both technological and theoretical perspectives with scientists from different fields teaming up for rapid assessment of potentially urgent situations. This paper focusses on the computational aspects of infectious disease models and applies commonly available graphics processing units (GPUs) for the simulation of these models. However, fully utilizing the resources of both CPUs and GPUs requires a carefully balanced heterogeneous approach.ResultsThe contribution of this paper is twofold. First, an efficient GPU implementation for evaluating a small-scale ODE model; here, the basic S(usceptible)-I(nfected)-R(ecovered) model, is discussed. Second, an asynchronous particle swarm optimization (PSO) implementation is proposed where batches of particles are sent asynchronously from the host (CPU) to the GPU for evaluation. The ultimate goal is to infer model parameters that enable the model to correctly describe observed data. The particles of the PSO algorithm are candidate parameters of the model; finding the right one is a matter of optimizing the likelihood function which quantifies how well the model describes the observed data. By employing a heterogeneous approach, in which both CPU and GPU are kept busy with useful work, speedups of 10 to 12 times can be achieved on a moderate machine with a high-end consumer GPU as compared to a high-end system with 32 CPU cores.ConclusionsUtilizing GPUs for parameter inference can bring considerable increases in performance using average host systems with high-end consumer GPUs. Future studies should evaluate the benefit of using newer CPU and GPU architectures as well as applying this method to more complex epidemiological scenarios.

Optimisation of military aircraft engine maintenance subject to engine part shortages using asynchronous metamodel-assisted particle swarm optimisation and Monte-Carlo simulations

ABSTRACTComprising the coordination of various maintenance levels, flight maintenance planning (FMP) constitutes an appealing and hard-to-solve optimisation problem. In this article, a modified version of the typical FMP problem is studied for a typical-size military fleet, focusing on engine maintenance: a disruption of the depot level maintenance (DLM) facilities is considered, causing long-term shortage of serviceable engine parts. In this context, an optimisation strategy is proposed to schedule engine and part exploitation so that engine availability be retained until the recovery of the DLM supply lines, and flight and maintenance requirements be fulfilled to the maximum possible extent. To incorporate the effects of stochastic events, model their interaction with the capacity of the available maintenance resources and assess the outcome of the proposed plans, Monte-Carlo simulations are employed. A computationally efficient variant of the asynchronous particle swarm optimisation method is introduced to find the optimal solutions, combining parallelisation of the evaluation calls with a metamodel-based pre-evaluation strategy to reduce the optimisation turnaround time. Tested on a full-scale application, the proposed method is shown to be capable of producing robust FMP solutions and, also, quantifying the maintenance chain's operating status to aid in long-term planning.

Microwave Imaging in Frequency Domain for Through-Wall Multiple Conductors

Abstract This paper presents an inverse scattering problem for through-wall imaging. Two separate perfect-conducting cylinders of unknown shapes are behind a homogeneous building wall and illuminated by the transverse magnetic (TM) plane wave. After an integral formulation, a discretization using the method of moment (MoM) is applied. The through-wall imaging (TWI) problem is recast as a nonlinear optimization problem with an objective function defined by the norm of a difference between the measured and calculated scattered electric field. Thus, the shape of the metallic cylinder can be obtained by minimizing the objective function. The asynchronous particle swarm optimization (APSO) is employed to find out the global extreme solution of the object function. Numerical results demonstrate that even when the initial guesses are far away from the exact shapes, and the multiple scattered fields between two conductors are serious, good reconstruction still can be obtained. In addition, the effect of Gaussian noise on the reconstruction result is investigated and the numerical simulation shows that even though the signal-noise ratio (SNR) is 20 dB, we can still get good results of reconstructions.

Study of runtime performance for Java-multithread PSO on multiCore machines

Optimisation meta-heuristics such as particle swarm optimisation (PSO) require high-performance computing (HPC). The use of software parallelism and hardware parallelism is mandatory to achieve HPC. Thread-level parallelism is a common software solution for programming on multicore systems. The Java language, which includes important aspects such as its portability and architecture neutrality, its multithreading facilities and its distributed nature, makes it an interesting language to parallel PSO. However, many factors may impact the runtime performance: the coding styles, the threads-synchronisation levels, the harmony between the software parallelism injected into the code and the available hardware parallelism, the Java networking APIs, etc. This paper analyses the Java runtime performance on handling multithread PSO over general purpose multicore machines and networked machines. Synchronous, asynchronous, single-swarm and multi-swarm PSO variants are considered.

Optimization of the Antenna Location for Relay Cooperative System by APSO

The channel capacity of a relay cooperative system with Maximum Ratio Combining (MRC) in a realistic indoor ultra-wideband communication is investigated by ray-tracing technique and Asynchronous Particle Swarm Optimization (APSO). We deploy amplify-and-forward relay to enhance the magnitude of signal and the propagation distance for a library indoor environment. When the transmitter is set in the center of this library environment without using relay, the outage probability of channel capacity is 49 %. The outage probabilities can be reduced to 18 % by using MRC of the cooperative relay, when the transmitter and relay are fixed in the center of the right half part and left half part respectively. Moreover, APSO is employed to search the best locations of the transmit antenna and relay antenna for reducing the outage probability. The outage probability can be reduced to zero by APSO algorithm in the numerical results.

Investigation on Digital Media Image Processing Algorithm Based on Asynchronous and Inertia Adaptive Particle Swarm Optimization

With the continuous development of computer science and technology, image processing and analysis gradually form the scientific system. Although history of image processing is not long, it attracts many researchers study on it. Digital media image widely exists in many fields, such as education, video, advertisement, and so on. Process digital media image is an important part of image processing. When analyze the digital media image, we want to extract the image part we care from the original image and then method for image segmentation is quite important. That is to say that the image segmentation will divide the image into a number of regions with specific and unique nature. How to keep the original characteristics of the digital media image is quite important in the image segmentation. In this paper, we propose a new algorithm for digital media image segmentation, and it is also can be used in the image processing. The algorithm is based on asynchronous particle swarm optimization algorithm to obtain the adaptive threshold; take the inertia factor into the algorithm, the optimal threshold has been acquired for the image segmentation. Compared with other particle swarm optimization algorithm, the algorithm has the advantages of stable, easy to converge to the optimal solution, and high segmentation speed.

Nondestructive Evaluation of Buried Dielectric Cylinders by Asynchronous Particle Swarm Optimization

Abstract This paper presents the study of time domain inverse scattering for a two-dimensional inhomogeneous dielectric cylinder buried in a slab medium via the finite difference time domain (FDTD) method and the asynchronous particle swarm optimization (APSO) method. For the forward scattering part, the FDTD method was employed to calculate the scattered E fields. Base on the scattering fields, these inverse scattering problems were transformed into optimization problems. The APSO method was applied to reconstruct the permittivity of the two-dimensional inhomogeneous dielectric cylinder. In addition, the effects of Gaussian noise on the reconstruction results were investigated. Numerical results show that even when the measured scattered fields were contaminated with Gaussian noise, APSO was able to yield good reconstructed quality.